Ruishi Chen @Ruishi_Ch

Joined October 2026

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Yucheng Jiang @YuchengJiang0

2 months ago

Computer-use traces are a rich source of knowledge about how everyday work gets done. They could help us document, audit, and reuse expertise that is otherwise lost. Modeling these traces poses three challenges: 1. raw traces consist of keyboard and mouse activities that carry little semantic meaning 2. real work is multi-threaded, with goals interleaved within a session 3. a trace shows the executed path, which does not directly reveal either the goal hierarchy that motivated each step or the control flow that organized the execution. Our paper introduces Task Model Induction (TMI), turning traces into symbolic, reusable task models. (EMNLP 2026)

YuchengJiang0 tweet picture

YuchengJiang0 tweet picture

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Dan Jurafsky @jurafsky

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Stanford University @Stanford

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CS PhD @Stanford @stanfordnlp | master @Stanford ｜ undergrad @UofIllinois | ex-research assistant @dmguiuc | ex-SWE @Apple

Yucheng Jiang @YuchengJiang0

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Max Palevsky Professor of Sociology, Computational & Data Science @UChicago, Santa Fe Institute, & Google tweeting about science, technology, and AI in society.

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Kellogg Chair of Technology at Kellogg, Founding director, Center for Science of Science and Innovation, Northwestern University

Dashun Wang @dashunwang

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Assistant Professor @Stanford CS @StanfordNLP @StanfordAILab Part time @humansand LLMs for Humans

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professor of computer science @Stanford @stanfordnlp, co-founder of @togethercompute, https://t.co/7R5THVnJ6u, @simile_ai, pianist

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